Why the study?
ECG device interpretations of AF, PVC, and PAC have high false-positive error rates, leaving an interpretation gap between ECG devices and cardiologists.
Does a deep learning-based ECG interpreter improve the precision of AF, PVC, and PAC detection compared to standard ECG-device generated interpretations?
Does a deep learning-based ECG interpreter improve the precision of AF, PVC, and PAC detection compared to standard ECG-device generated interpretations?
A deep learning-based ECG interpreter improves the precision of detecting atrial fibrillation, premature ventricular contractions, and premature atrial contractions compared to standard automated ECG device interpretations.
BACKGROUND Based on the clinical investigation in this study, electrocardiography (ECG) devices generated interpretations on atrial fibrillation (AF), premature ventricular contraction (PVC), and premature atrial contraction (PAC) have high ratios of false-positive errors. An ECG interpretation gap exists between ECG devices and cardiologists. OBJECTIVE The aim of this study was to develop an electrocardiogram (ECG) interpreter to improve the performance of AF, PVC, and PAC screening based on an ECG. METHODS In this study, we first adopted a deep learning model to delineate ECG features such as P, QRS, and T waves based on 1160 8–10-s lead I or lead II ECG signals from a clinically-used 12-lead ECG device whose ECG device interpretation is AF as a training dataset. Second, a sliding window with 3-RR intervals in length is applied to the raw ECG to examine the delineated features in the window, and the ECG interpretation is then determined based on experiences of cardiologists. RESULTS The results indicate the following: (1) This delineator achieves a good performance on P-, QRS-, and T- wave delineation with a sensitivity/specificity of 0.94/0.98, 1.00/0.99, and 0.97/0.98, respectively, in 48 10-s test ECGs mixed with true-positive AF and false-positive AF ECGs. (2) As compared to ECG-device generated interpretations, the precision of the detection of AF, PVC, and PAC in this study was increased from 0.77 to 0.86, 0.76 to 0.84, and 0.82 to 0.87 in 188 10-s test ECGs. Finally, (3) the F1 measure, which is a measure of the accuracy of test data but takes false- positives and false-negative into account, on the detection of AF, PVC, and PAC were 0.92, 0.91, and 0.83, respectively. CONCLUSIONS In conclusion, this study improved the precision of ECG devices’ interpretation and bridge the gap between machines’ ECG interpretation and cardiologists and can facilitate clinical applications on one-channel ECG. CLINICALTRIAL
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Hong et al. (2021) studied this question.
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